1. The Naturalistic Turn in Philosophy of Science 2. The Framework of Mechanistic Explanation: Parts, Operations, and Organization 3. Representing and Reasoning About Mechanisms 4. Mental Mechanisms: Mechanisms that Process Information 5. Discovering Mental Mechanisms 6 . Summary.

2. Daugman, J. G. Brain metaphor and brain theory 3. Mundale, J. Neuroanatomical Foundations of Cognition: Connecting the Neuronal Level with the Study of Higher Brain Areas.

1. A Historical Look at Unity 2. Field Guide to Modern Concepts of Reduction and Unity 3. Kitcher's Revisionist Account of Unification 4. Critics of Unity 5. Integration Instead of Unity 6. Reduction via Mechanisms 7. Case Studies in Reduction and Unification across the Disciplines.

This paper defends cognitive neuroscience’s project of developing mechanistic explan- ations of cognitive processes through decomposition and localization against objections raised by William Uttal in The New Phrenology. The key issue between Uttal and researchers pursuing cognitive neuroscience is that Uttal bets against the possibility of decomposing mental operations into component elementary operations which are localized in distinct brain regions. The paper argues that it is through advancing and revising what are likely to be overly simplistic and incorrect decompositions that (...) the goals of cognitive neuroscience are likely to be achieved. (shrink)

Functionalists in philosophy of mind traditionally raise two major arguments against the type identity theory: (1) psychological states are _multiply realizable_ so that there are no one-to-one mappings of psychological states onto neural states and (2) the most that evidence could ever establish is the _correlation_ of psychological and neural states, not their identity. We defend a variant on the traditional type identity theory which we call _heuristic identity theory_ (HIT) against both of these objections. Drawing its inspiration from scientific (...) practice, heuristic identity theory construes identity claims as hypotheses that guide subsequent inquiry, not as conclusions of the research. (shrink)

It is no secret that scientists argue. They argue about theories. But even more, they argue about the evidence for theories. Is the evidence itself trustworthy? This is a bit surprising from the perspective of traditional empiricist accounts of scientific methodology according to which the evidence for scientific theories stems from observation, especially observation with the naked eye. These accounts portray the testing of scientific theories as a matter of comparing the predictions of the theory with the data generated by (...) these observations, which are taken to provide an objective link to reality. (shrink)

Neuroscience and cognitive science seek to explain behavioral regularities in terms of underlying mechanisms. An important element of a mechanistic explanation is a characterization of the operations of the parts of the mechanism. The challenge in characterizing such operations is illustrated by an example from the history of physiological chemistry in which some investigators tried to characterize the internal operations in the same terms as the overall physiological system while others appealed to elemental chemistry. In order for biochemistry to become (...) successful, researchers had to identify a new level of operations involving operations over molecular groups. Existing attempts at mechanistic explanation of behavior are in a situation comparable to earlier approaches to physiological chemistry, drawing their inspiration either from overall psychology activities or from low-level neural processes. Successful mechanistic explanations of behavior require the discovery of the appropriate component operations. Such discovery is a daunting challenge but one on which success will be beneficial to both behavioral scientists and cognitive and neuroscientists. (shrink)

Cognitive science is, more than anything else, a pursuit of cognitive mechanisms. To make headway towards a mechanistic account of any particular cognitive phenomenon, a researcher must choose among the many architectures available to guide and constrain the account. It is thus fitting that this volume on contemporary debates in cognitive science includes two issues of architecture, each articulated in the 1980s but still unresolved: " • Just how modular is the mind? – a debate initially pitting encapsulated mechanisms against (...) highly interactive ones. • Does the mind process language-like representations according to formal rules? – a debate initially pitting symbolic architectures against less language-like architectures. " Our project here is to consider the second issue within the broader context of where cognitive science has been and where it is headed. The notion that cognition in general—not just language processing—involves rules operating on language-like representations actually predates cognitive science. In traditional philosophy of mind, mental life is construed as involving propositional attitudes—that is, such attitudes towards propositions as believing, fearing, and desiring that they be true—and logical inferences from them. On this view, if a person desires that a proposition be true and believes that if she performs a certain action it will become true, she will make the inference and perform the action. (shrink)

The claim of the multiple realizability of mental states by brain states has been a major feature of the dominant philosophy of mind of the late 20th century. The claim is usually motivated by evidence that mental states are multiply realized, both within humans and between humans and other species. We challenge this contention by focusing on how neuroscientists differentiate brain areas. The fact that they rely centrally on psychological measures in mapping the brain and do so in a comparative (...) fashion undercuts the likelihood that, at least within organic life forms, we are likely to find cases of multiply realized psychological functions. (shrink)

trying to explain these reactions in terms of changes in ele- began trying to characterize physiological processes in.

New research tools such as PET can produce dramatic results. But they can also produce dramatic artifacts. Why is PET to be trusted? We examine both the rationale that justifies interpreting PET as measuring brain activity and the strategies for interpreting PET results functionally. We show that functional ascriptions with PET make important assumptions and depend critically on relating PET results to those secured through other research techniques.

Contemporary epistemology has assumed that knowledge is represented in sentences or propositions. However, a variety of extensions and alternatives to this view have been proposed in other areas of investigation. We review some of these proposals, focusing on (1) Ryle's notion of knowing how and Hanson's and Kuhn's accounts of theory-laden perception in science; (2) extensions of simple propositional representations in cognitive models and artificial intelligence; (3) the debate concerning imagistic versus propositional representations in cognitive psychology; (4) recent treatments of (...) concepts and categorization which reject the notion of necessary and sufficient conditions; and (5) parallel distributed processing (connectionist) models of cognition. This last development is especially promising in providing a flexible, powerful means of representing information nonpropositionally, and carrying out at least simple forms of inference without rules. Central to several of the proposals is the notion that much of human cognition might consist in pattern recognition rather than manipulation of rules and propositions. (shrink)

In setting a framework for the papers that follow, I have explored some of the major characteristics of disciplines and the factors that breed ethnocentrism among disciplines, considered what factors can lead researchers to cross disciplinary boundaries, and explored the kinds of conceptual as well as social and institutional products that result from cross-disciplinary work. While drawing out the significance of these various considerations for psycholinguistics, I have presented a fairly general conceptual analysis that is not restricted to this case. (...) The following papers consider in much more detail the particular features of the historical and current endeavors in developing psycholinguistics. (shrink)

New research tools such as PET can produce dramatic results. But they can also produce dramatic artifacts. Why is PET to be trusted? We examine both the rationale that justifies interpreting PET as measuring brain activity and the strategies for interpreting PET results functionally. We show that functional ascriptions with PET make important assumptions and depend critically on relating PET results to those secured through other research techniques.

Many studies of language, whether in philosophy, linguistics, or psychology, have focused on highly developed human languages. In their highly developed forms, such as are employed in scientific discourse, languages have a unique set of properties that have been the focus of much attention. For example, descriptive sentences in a language have the property of being "true" or "false," and words of a language have senses and referents. Sentences in a language are structured in accord with complex syntactic rules. Theorists (...) focusing on language are naturally led to ask questions such as what constitutes the meanings of words and sentences and how are the principles of syntax encoded in the heads of language users. While there is an important function for inquiries into the highly developed forms of these cultural products (Abrahamsen, 1987), such a focus can be quite misleading when we want to explain how these products have arisen or the human capacity to use language. The problem is that focusing on its most developed forms makes linguistic ability seem to be a _sui generis_ phenomenon, not related to, and hence not explicable in terms of other cognitive capacities. Chomsky's (1980) postulation of a specific language module equipped with specialized resources needed to process language and possessed only by hum ans is not a surprising result. (shrink)

(1983). Consciousness and complexity: Evolutionary perspectives on the mind-body problem. Australasian Journal of Philosophy: Vol. 61, No. 4, pp. 378-395.

Much of cognitive neuroscience as well as traditional cognitive science is engaged in a quest for mechanisms through a project of decomposition and localization of cognitive functions. Some advocates of the emerging dynamical systems approach to cognition construe it as in opposition to the attempt to decompose and localize functions. I argue that this case is not established and rather explore how dynamical systems tools can be used to analyze and model cognitive functions without abandoning the use of decomposition and (...) localization to understand mechanisms of cognition. (shrink)

For many people, consciousness is one of the defining characteristics of mental states. Thus, it is quite surprising that consciousness has, until quite recently, had very little role to play in the cognitive sciences. Three very popular multi-authored overviews of cognitive science, Stillings et al. [33], Posner [26], and Osherson et al. [25], do not have a single reference to consciousness in their indexes. One reason this seems surprising is that the cognitive revolution was, in large part, a repudiation of (...) behaviorism's proscription against appealing to inner mental events. When researchers turned to consider inner mental events, one might have expected them to turn to conscious states of mind. But in fact the appeals were to postulated inner events of information processing. The model for many researchers of such information processing is the kind of transformation of symbolic structures that occurs in a digital computer. By positing procedures for performing such transformation of incoming information, cognitive scientists could hope to account for the performance of cognitive agents. Artificial intelligence, as a central discipline of cognitive science, has seemed to impose some of the toughest tests on the ability to develop information processing accounts of cognition: it required its researchers to develop running programs whose performance one could compare with that of our usual standard for cognitive agents, human beings. As a result of this focus, for AI researchers to succeed, at least in their primary task, they did not need to attend to consciousness; they simply had to design programs that behaved appropriately (no small task in itself!). This is not to say that conscious was totally ignored by artificial intelligence researchers. Some aspect of our conscious experience seemed critical to the success of any information processing model. For example, conscious agents exhibit selective attention. Some information received through their senses is attended to; much else is ignored.. (shrink)

In the wake of the cognitivist revolution in psychology, a number of philosophers (e.g., Putnam and Fodor) have argued that the functional ontology underlying cognitivism allows for the autonomy of psychology from neuroscience. It is contended that these arguments do not support the kind of autonomy proposed and that, in any case, such autonomy would be misguided. The last claim is supported by considering the consequences such autonomy would have for a number of research programmes in cognitive psychology. It is (...) argued that these programmes might benefit from neuroscience. The manner in which this benefit can be acquired without requiring that psychology be reduced to neuroscience is sketched. (shrink)

The reemergence of connectionism² has profoundly altered the philosophy of mind. Paul Churchland has argued that it should equally transform the philosophy of science. He proposes that connectionism offers radical and useful new ways of understanding theories and explanations.

The notion that the mind is a physical symbol system (Newell) with a determinate functional architecture (Pylyshyn) provides a compelling conception of the relation of cognitive inquiry to neuroscience inquiry: cognitive inquiry explores the activity within the symbol system while neuroscience explains how the symbol system is realized in the brain. However, the view the the mind is a physical symbol system is being challenged today by researchers in artificial intelligence who propose that the mind is a connectionist system and (...) not simply a rule processing system. I describe this challenge and offer evidence that indicates the challenge may be well motivated. I then turn to the question of how such changes in the conception of the activity of the mind will affect our understanding of the relation of neuroscience to cognitive inquiry and sketch a framework in which the cognitive system consists of several levels and in which both neuroscience and cognitive science can make contributions at several of these levels. (shrink)